1. Field of the Invention
This invention generally relates to detection of moving targets, and more specifically to detection of moving targets using one or more acoustic arrays. More particularly, this invention pertains to a system that uses conventional and/or adaptive beamforming to locate and count multiple targets in dynamic, noisy, and highly mobile environments.
2. Background of the Invention
Acoustic over watch sensor systems are used to provide extended range surveillance, detection and identification for force protection and tactical security on the battlefield. A network of over watch sensors remotely deployed in conjunction with a central processing node (or gateway) can provide early warning and assessment of enemy threats, near real-time situational awareness to commanders, and may reduce potential hazards to the soldier. When a network of acoustic sensors is used, measurements from distributed arrays are reported to a “fusion” center that sorts the sensor measurements and estimates the location and kinematics of identified sources of interest.
An essential requirement for unattended acoustic sensors systems used for battlefield surveillance is the capability to locate and track multiple targets in dynamic, noisy, and highly mobile environments. Today's state-of-the-art acoustic sensor systems report measurements from diverse sources comprising targets of interest, background noise (or clutter), and noise due to local environmental conditions. However, complex data association logic is required to sort out sensor measurements into general categories such as targets of interest, sources that are not of interest and false detections that cannot be correlated over time.
Target tracking in noisy, cluttered environments is treated as a problem in associating detected target observations with target tracks. These observations could be comprised of false target detections or an unknown number of actual target detections. Consequently, the performance of these methods is fundamentally dependent on the system's target detection capability.
Conventional and adaptive beamforming algorithms are commonly used to enhance the directivity and direction finding capability of a single sensor array and are essential in realizing the performance benchmarks required in detecting and localizing remote targets of interest. These spatial filtering methods allow position-related measurements such as bearing to a target to be computed from the steered response of an acoustic array. One of the most challenging areas of the multi-sensor performance problem is the beam-pointing control of an acoustic array in dense multiple target environments, where closely spaced targets, highly maneuvering targets, and targets in the presence of multi-path should be resolved. Factors that contribute to this challenge are finite sensor resolution, inconsistent beam shape, false alarms, complex target formations, and the mobile nature of target signatures.
The use of multiple acoustic sensors in array configurations for the purpose of forming specific narrow look directions (beamforming) and using such means to extend the detection range of individual microphones has been implemented and tested for land acoustic vehicle detection. Such implementations have been successful in identifying the presence of multiple targets in the local vicinity of the acoustic array by measuring multiple bearing directions to each target with respect to the acoustic array. Some implementations also determine a passive range estimation to target. By combining this passive range estimation with bearing to target information, multiple tracks to targets can be maintained. The number of targets that can be effectively tracked by an acoustic array using adaptive beamforming methods is widely recognized as the quantity (N−1) where N is the number of microphones in the array. Consequently, if more than N−1 targets enter the surveillance area, confusion potentially exists and the ability to individually track each vehicle cannot be maintained.
In practice, a basic problem of accountability occurs when a count for the number of targets that pass by the vicinity of the acoustic array is desired. Often a user must examine the ongoing real-time output of the sensor array to determine a best guess for the number of vehicles that were tracked during any particular timeframe. Therefore, a man-in-the-loop is required to perform very careful examination of the ongoing information. Often, the tracks are not maintained continuously and without high fidelity (depending on range from the array and spacing of targets). Consequently, it is difficult to know which tracks should be “counted”.
If more than one acoustic array is required to cover a greater area, accountability of target count becomes very difficult. To mitigate this issue, multiple microphone arrays are generally networked together to improve the effective area of coverage. Networked acoustic arrays that combine their respective bearing information to determine more accurate target tracking (using triangulation) should agree on which targets they are tracking from remote locations. Deciding if the target is the same target being viewed from multiple remote locations is inherently difficult, often creating “ghost” targets, which are targets that don't exist.
The sharing of tracking information from multiple sensor sites requires that each site resolve individual targets. In scenarios where targets are closely spaced or moving abreast relative to a sensor node, the sensors do not “hear” the same target over portions of the track through the sensor field, therefore, tracking and target counting accountability is very difficult.
What is needed are signal processing techniques that would provide over watch sentries with the ability to autonomously and remotely count the number of vehicles passing through a predetermined field of view. The need for such a system has heretofore remained unsatisfied.